XXII ISPRS Congress 2012: Technical Commission VII

Bibliographic data

Multivolume work

Persistent identifier:: 1663813779

Title:: XXII ISPRS Congress 2012

Sub title:: Melbourne, Australia, 25 August-1 September 2012

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663813779

Language:: English

Additional Notes:: Kongress-Thema: Imaging a sustainable future

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Document type:: Multivolume work

Volume

Persistent identifier:: 1663821976

Title:: Technical Commission VII

Scope:: 546 Seiten

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663821976

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(39,B7)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist ermittelt.; Literaturangaben

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [VII/4: METHODS FOR LAND COVER CLASSIFICATION]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: MODELING SPATIAL DISTRIBUTION OF A RARE AND ENDANGERED PLANT SPECIES (Brainea insignis) IN CENTRAL TAIWAN Wen-Chiao Wang, Nan-Jang Lo, Wei-I Chang, Kai-Yi Huang

Document type:: Multivolume work

Structure type:: Chapter

dal ote 0). nd Ce, ids the ral 13, ch, ive lds ts, ice 25, ry. do itu ce. 2). ile International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia MODELING SPATIAL DISTRIBUTION OF A RARE AND ENDANGERED PLANT SPECIES (Brainea insignis) IN CENTRAL TAIWAN Wen-Chiao Wang?, Nan-Jang Lo", Wei-I Chang, Kai-Yi Huang? “Graduate student, Dept. of Forestry, Chung-Hsing University, Taiwan E-mail: chiao87219 € yahoo.com.tw "Specialist, EPMO, Chung-Hsing University, Taiwan E-mail: njl Gdragon.nchu.edu.tw * Director, HsinChu FDO, Forest Bureau, Council of Agriculture, Taiwan E-mail: weii @ forest.gov.tw Professor, same as with author-a E-mail: kyhuang 9 dragon.nchu.edu.tw (corresponding author) 250 Kuo-Kuang Road, Taichung, Taiwan 402, Tel: +886-4-22854663; Fax: +886-4-22854663 Commissions: VII/4 methods for land cover classification KEY WORDS: Forestry, Ecology, Modeling, Prediction, Algorithms, Pattern, Performance, Accuracy. ABSTRACT: With an increase in the rate of species extinction, we should choose right methods that are sustainable on the basis of appropriate science and human needs to conserve ecosystems and rare species. Species distribution modeling (SDM) uses 3S technology and statistics and becomes increasingly important in ecology. Brainea insignis (cycad-fern, CF) has been categorized a rare, endangered plant species, and thus was chosen as a target for the study. Five sampling schemes were created with different combinations of CF samples collected from three sites in Huisun forest station and one site, 10 km farther north from Huisun. Four models, MAXENT, GARP, generalized linear models (GLM), and discriminant analysis (DA), were developed based on topographic variables, and were evaluated by five sampling schemes. The accuracy of MAXENT was the highest, followed by GLM and GARP, and DA was the lowest. More importantly, they can identify the potential habitat less than 10% of the study area in the first round of SDM, thereby prioritizing either the field-survey area where microclimatic, edaphic or biotic data can be collected for refining predictions of potential habitat in the later rounds of SDM or search areas for new population discovery. However, it was shown unlikely to extend spatial patterns of CFs from one area to another with a big separation or to a larger area by predictive models merely based on topographic variables. Follow-up studies will attempt to incorporate proxy indicators that can be extracted from hyperspectral images or LIDAR DEM and substitute for direct parameters to make predictive models applicable on a broader scale. 1. INTRODUCTION Biodiversity is very important for humans and all other species on the Earth. Without the diversity of species, ecosystems are more fragile to natural disasters and climatic change. With an increase in the rate of species extinction, we must conserve ecosystems and rare species by choosing right methods that are sustainable on the basis of appropriate science and human needs. Forest resources in Taiwan are very abundant, but environmental carrying capacity of the island is vulnerable, thus when using them we must think of conservation at the same time. Species distribution modeling (SDM) could apply in conservation and protection rare species, ecology, epidemiology, disaster and management in forestry (Pearson ef al., 2007; Asner et al., 2008; Cayuela er al., 2000). SDM needs to utilize the combination of 3S technology and statistics, and has become increasingly important in ecology (Cóté and Reynolds, 2002; Guisan and Thuiller, 2005). Nowadays a variety of statistical methods have been used to model ecological niches and predict the geographical distributions of species, such as BIOCLIM, maximum entropy (MAXENT), DOMAIN, genetic algorithm for rule-set prediction (GARP), generalized linear models (GLM), generalized additive model (GAM) and discriminant analysis (DA) (Elith er. al, 2006; Hernandez et al, 2006; Guisan et al, 2007; Peterson et al, 2007; Wisz et al., 2008; Ke et al., 2010). SDM is based on the environmental conditions of known sites to predict unknown area, and also to identify the relationship between the species and environment. The distribution pattern of natural vegetation is associated with four types of environmental factors, including climatic, physiographic, edaphic, and biotic factors (Su, 1987). For SDM, it is desirable to predict a species distribution on the basis of ecological (direct) parameters (i.e. climate, soil, and biotic factor) that are to be the causal, driving forces for its distribution. Data for such direct parameters, however, are generally difficult or expensive to measure, soil data are even more difficult to derive, and they tend to be less accurate than pure topographic characteristics (Guisan and Zimmermann, 2000). Moreover, biotic factor is extremely difficult to estimate due to the fine spatiotemporal resolution and potentially complex nature of biotic dimensions (Barve er. al., 2011). On the other hand, indirect parameters (e.g. topographic variables: elevation, slope, aspect) are most easily measured by remote sensing and are often used because of their good correlation with observed species patterns (Guisan and Zimmermann, 2000) Hence, SDM should be run on an iterative basis with topographic data in initial rounds and climatic data, soil data, or biotic data, when available, in later rounds since not all the data needed by SDM for the four types of factors aforementioned are readily available at one time. In this study, we used four methods: MAXENT, GARP, GLM, and DA to build models and to predict the potential habitat of a rare plant together with five different sampling schemes. Our study area falls within a homogeneous climatic zone with one degree of latitude; therefore, we took account of the area's microclimate, which in turn affects species’ distribution. Indeed, the topography of an area influneces the microclimate of that area (Su, 1987). Furthermore, fine spatial-resolution soil data and biotic data were not available up to the present. Hence, we did run the four aforementioned SDM models on an iterative basis by incorporating elevation, slope, aspect, terrain position, and vegetation index derived from SPOT images in the first round. We designed five sampling schemes from two areas: 1) a small range with the distance of 0.7 km between sampling sites and 2) a large range with the distance of about 10 km between sampling sites. We evaluated these models in

Access restriction

Copyright

fullscreen: Technical Commission VII (B7)

Multivolume work

Volume

Chapter

Chapter

Table of contents

Cite and reuse

Volume

Chapter

Image

Image fragment

Citation links

Citation recommendation